Author ORCID Identifier

https://orcid.org/0009-0003-8152-2547

Semester

Fall

Date of Graduation

2025

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Civil and Environmental Engineering

Committee Chair

Kevin Orner

Committee Member

Lian-Shin Lin

Committee Member

Emily Garner

Committee Member

Oishi Sanyal

Committee Member

Nicolas Zegre

Abstract

Wastewater management in small communities has faced numerous challenges in recent years, including population growth, limited budgets for operation and maintenance, and stringent effluent standards required by environmental regulations. These challenges motivate enhancing wastewater treatment processes to protect water quality. Wastewater treatment performance can be improved in small towns and rural regions by implementing data-driven strategies. Data science, life cycle assessment (LCA), and life cycle cost analysis (LCCA) are three data-driven decision-making tools that require data collection, modeling, computational models, and statistical analysis that could be utilized to improve wastewater treatment efficiencies. In this study, these three tools were adopted to achieve three different targets in wastewater management. In the first project, data science (e.g., machine learning) was applied to predict nutrient recovery efficiency from different types of anaerobic digestion process effluent “digestates” via struvite precipitation. Struvite precipitation converts digestate wastewater into valuable fertilizer. Five statistical and machine learning (ML) models were employed to quantify the effectiveness of predicting the percentage of nutrients recovered from wastewater digestate derived from different organic waste streams via struvite precipitation. These five models were multiple linear regression (MLR), polynomial regression (PLR), K-nearest neighbors (KNN), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The results of the study revealed that RF and XGBoost had the best performance in predicting nutrient recovery efficiency. Both models had a regression coefficient (R2) for phosphate and ammonium recoveries above 0.90 and a root mean square error of 2–7.67.

In the second project, the sustainability of three different centralized wastewater treatment alternatives in Monteverde, Costa Rica, was evaluated by using a modified version of the social, economic, and environmental wastewater decision support system tool (SEE WWDST). The study considered three potential post-treatment units for treating the UASB reactor effluent: (i) an aeration tank, (ii) a constructed wetland, and (iii) a trickling filter. A single SEE impact score was produced through the tool by using a multi-criteria decision analysis. The SEE impact score evaluated twelve sustainability metrics across three main categories: economic performance, environmental performance, and social performance. Following that, the systems were assessed using equal social, environmental, and economic preferences. The results showed that the trickling filter system was the highest scoring option.

In the third project, another modified version of SEE WWDST was used to compare six different decentralized WWTP systems in Costa Rica. The evaluated technologies included septic tanks and composting toilets. The results showed that the composting toilets remained robust across different stakeholder priorities and sustainability considerations, with lower SEE impact scores compared to the septic systems.

The findings of this dissertation emphasize the importance of using data-driven analyses and sustainable development tools such as LCA and LCCA in managing wastewater in small communities. This can provide decision-makers with a better understanding of how to achieve the most sustainable wastewater practices. As a result, this can facilitate progress toward multiple United Nations Sustainable Development Goals, including SDG 6 (Clean Water and Sanitation), SDG 14 (Life Below Water), and SDG 11 (Sustainable Cities and Communities).

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